2 citations · 3 across the 16 of their papers we have counts for
22 papers
Grid-Bench: Can LLMs Predict How Discrete Worlds Change?
Zixiang Xu, Zirui Song, Wanrong Zheng +16
Spatial instructions describe how to move objects, transform regions, or modify their surroundings. When several operations are applied in sequence, each acts on a configuration sh…
AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes
Zhuoxuan Zhang, Jinhao Duan, Xinyu Zhao +7
Generative recommendation models based on autoregressive decoding have been adopted as alternatives to classical ranking-based systems. While their modeling flexibility has been wi…
Exploring the Design Space of Permutation Diffusion LLMs
Zhe Xu, Mingfu Liang, Kavosh Asadi +16
Permutation generation with language models requires both text-conditioned ranking and a global one-to-one assignment between candidates and output positions. We study this problem…
Iterative Policy Refinement through Semantic Rollout Analysis
Feiyu Gavin Zhu, Qi Xu, Zhifei Deng +4
Structured policies improve efficiency, robustness, and interpretability in imitation learning by introducing task-specific inductive bias, but existing structure generation method…
Recursive Self-Improvement via On-Policy Distillation for Reasoning
Shangjian Yin, Zehao Zhao, Kavosh Asadi +7
On-policy distillation (OPD) trains a student model by having it generate trajectories, then matching its next-token predictions with an external teacher's next-token predictions.…
SPD: Single Pass Decoding for Generative Reranking
Emil Laftchiev, Prachi Agrawal, Moe Kayali +7
Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive decoding spends one sequential fo…
The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers
Zhe Xu, Prachi Agrawal, Kavosh Asadi +17
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains b…
Tokenizing Numerical and Embedding Features for LLM RecSys
Zhe Xu, Ankit Peshin, Chiyu Zhang +7
Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilit…
Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
Zhuoxuan Zhang, Kangqi Ni, Yuhang Chen +12
Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autore…
Self-Guided Test-Time Training for Long-Context LLMs
Xinyu Zhu, Zhe Xu, Xiaohan Wei +10
Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long…
SCOReD: Student-Aware CoT Optimization for Recommendation Distillation
Haz Sameen Shahgir, Yufei Li, Xiaohan Wei +8
Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to this task. Large teachers approac…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference
Yuhang Chen, Jinhao Duan, Ruichen Zhang +11
Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environm…
Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation
Yuhang Chen, Xianfeng Wu, Jinhao Duan +11
Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. However, such promising frameworks…
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Yihua Zhang, Mingfu Liang, Jiyan Yang +11
Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.…
Learning Evidence Highlighting for Frozen LLMs
Shaoang Li, Yanhang Shi, Yufei Li +10
Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts. We introduce HiLight, an Evidence Emphasis framework that…
How Well Does Generative Recommendation Generalize?
Yijie Ding, Zitian Guo, Jiacheng Li +8
A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. However, there is few systematic…
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs
Zijie Liu, Jie Peng, Jinhao Duan +7
Sparse Mixture-of-Experts (SMoE) architectures are increasingly used to scale large language models efficiently, delivering strong accuracy under fixed compute budgets. However, SM…
GR2: Generative Reasoning Re-ranker
Mingfu Liang, Yufei Li, Jay Xu +20
Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work h…
Large Scale Retrieval for the LinkedIn Feed using Causal Language Models
Sudarshan Srinivasa Ramanujam, Antonio Alonso, Saurabh Kataria +20
In large scale recommendation systems like the LinkedIn Feed, the retrieval stage is critical for narrowing hundreds of millions of potential candidates to a manageable subset for…
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song +17
Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Al…
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation
Hamed Firooz, Maziar Sanjabi, Adrian Englhardt +20
Ranking and recommendation systems are the foundation for numerous online experiences, ranging from search results to personalized content delivery. These systems have evolved into…